{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import h5py\n",
    "import scipy.io\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "matFilename = './Data/2018-04-12_batchdata_updated_struct_errorcorrect.mat'\n",
    "f = h5py.File(matFilename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#refs#', '#subsystem#', 'batch', 'batch_date']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(f.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "batch = f['batch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Vdlin',\n",
       " 'barcode',\n",
       " 'channel_id',\n",
       " 'cycle_life',\n",
       " 'cycles',\n",
       " 'policy',\n",
       " 'policy_readable',\n",
       " 'summary']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(batch.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Program Files\\Python37\\lib\\site-packages\\h5py\\_hl\\dataset.py:313: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.\n",
      "  \"Use dataset[()] instead.\", H5pyDeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "num_cells = batch['summary'].shape[0]\n",
    "bat_dict = {}\n",
    "for i in range(num_cells):\n",
    "    cl = f[batch['cycle_life'][i,0]].value\n",
    "    policy = f[batch['policy_readable'][i,0]].value.tobytes()[::2].decode()\n",
    "    summary_IR = np.hstack(f[batch['summary'][i,0]]['IR'][0,:].tolist())\n",
    "    summary_QC = np.hstack(f[batch['summary'][i,0]]['QCharge'][0,:].tolist())\n",
    "    summary_QD = np.hstack(f[batch['summary'][i,0]]['QDischarge'][0,:].tolist())\n",
    "    summary_TA = np.hstack(f[batch['summary'][i,0]]['Tavg'][0,:].tolist())\n",
    "    summary_TM = np.hstack(f[batch['summary'][i,0]]['Tmin'][0,:].tolist())\n",
    "    summary_TX = np.hstack(f[batch['summary'][i,0]]['Tmax'][0,:].tolist())\n",
    "    summary_CT = np.hstack(f[batch['summary'][i,0]]['chargetime'][0,:].tolist())\n",
    "    summary_CY = np.hstack(f[batch['summary'][i,0]]['cycle'][0,:].tolist())\n",
    "    summary = {'IR': summary_IR, 'QC': summary_QC, 'QD': summary_QD, 'Tavg':\n",
    "                summary_TA, 'Tmin': summary_TM, 'Tmax': summary_TX, 'chargetime': summary_CT,\n",
    "                'cycle': summary_CY}\n",
    "    cycles = f[batch['cycles'][i,0]]\n",
    "    cycle_dict = {}\n",
    "    for j in range(cycles['I'].shape[0]):\n",
    "        I = np.hstack((f[cycles['I'][j,0]].value))\n",
    "        Qc = np.hstack((f[cycles['Qc'][j,0]].value))\n",
    "        Qd = np.hstack((f[cycles['Qd'][j,0]].value))\n",
    "        Qdlin = np.hstack((f[cycles['Qdlin'][j,0]].value))\n",
    "        T = np.hstack((f[cycles['T'][j,0]].value))\n",
    "        Tdlin = np.hstack((f[cycles['Tdlin'][j,0]].value))\n",
    "        V = np.hstack((f[cycles['V'][j,0]].value))\n",
    "        dQdV = np.hstack((f[cycles['discharge_dQdV'][j,0]].value))\n",
    "        t = np.hstack((f[cycles['t'][j,0]].value))\n",
    "        cd = {'I': I, 'Qc': Qc, 'Qd': Qd, 'Qdlin': Qdlin, 'T': T, 'Tdlin': Tdlin, 'V':V, 'dQdV': dQdV, 't':t}\n",
    "        cycle_dict[str(j)] = cd\n",
    "        \n",
    "    cell_dict = {'cycle_life': cl, 'charge_policy':policy, 'summary': summary, 'cycles': cycle_dict}\n",
    "    key = 'b3c' + str(i)\n",
    "    bat_dict[key]=   cell_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['b3c0', 'b3c1', 'b3c2', 'b3c3', 'b3c4', 'b3c5', 'b3c6', 'b3c7', 'b3c8', 'b3c9', 'b3c10', 'b3c11', 'b3c12', 'b3c13', 'b3c14', 'b3c15', 'b3c16', 'b3c17', 'b3c18', 'b3c19', 'b3c20', 'b3c21', 'b3c22', 'b3c23', 'b3c24', 'b3c25', 'b3c26', 'b3c27', 'b3c28', 'b3c29', 'b3c30', 'b3c31', 'b3c32', 'b3c33', 'b3c34', 'b3c35', 'b3c36', 'b3c37', 'b3c38', 'b3c39', 'b3c40', 'b3c41', 'b3c42', 'b3c43', 'b3c44', 'b3c45'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bat_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The history saving thread hit an unexpected error (OperationalError('database is locked')).History will not be written to the database.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25a93d96be0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bat_dict['b3c43']['summary']['cycle'], bat_dict['b3c43']['summary']['QD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x25b81925518>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Xkekj9OH+82cOUFtRyLvPm5XqUkREpkyow33f8U7+vK+Fv7msjpzsUB+qiMibTJh4ZlZgZs+a2QtmtsvMvhmnzRfNbLeZbTezfzezBckpd3LufeYgedlZfOgS3c5XRKaXIN3ZfuAad78AuBBYZWaXj2nzPNDg7iuAB4DvJrbMyesbjPCb549ww/mzqSrJT3U5IiJTasJw96iu2NPc2MPHtFnn7j2xpxuBlF8Cun7vcU70DfFXF6W8FBGRKRdoINrMss1sG3AceMLdN52i+a3AY4ko7kw8tPUINaX5/KezqlJdiojIlAsU7u4ecfcLifbILzWz5fHamdlHgAbgznH2rzazzWa2uamp6XRrnlBb9wDr9h7npgvm6kSqiExLk0o+d28H1gOrxu4zs+uArwE3unv/OJ+/xt0b3L2hpiZ5FxQ9uqORwYjzlytrk/YeIiLpLMhsmRozq4htFwLXAS+OabMS+DHRYD+ejEIn4/GdjSyqKea8uWWpLkVEJCWC9NznAOvMbDvwHNEx90fN7FtmdmOszZ1ACfBrM9tmZmuTVO+ETvQNsml/K+9aNktXpIrItDXhAqLuvh1YGef1O0ZtX5fguk7bUy81MTTsXHeurkgVkekrdGcbn9x9jMqiXC6qq0x1KSIiKROqcB+MDLNubxNXL52pBTlEZFoLVbg/uOUwHb2DvG/F3FSXIiKSUuEK962HWTq7VPdtF5FpL1Th3jMQYVZZgWbJiMi0F5pw7xuM8PKxLs6ZXZrqUkREUi404b71UBsDkWEuWzgj1aWIiKRcaML9keePkp+TxSUKdxGR8IT72heOcuMFcykryE11KSIiKReKcO8bjNA7GGFOeUGqSxERSQuhCPfHd74OwKULde92EREISbjvPdZJTpZx5WKFu4gIhCTcu/qGKMzN1vx2EZGYUIT7C4fbNb9dRGSUjA/3Qy09bD/cwbuW6Ra/IiIjMj7cGzt6AVheW57iSkRE0keQZfYKzOxZM3vBzHaZ2TfjtMk3s1+a2T4z22Rm9ckoNp6XjncBUFOaP1VvKSKS9oL03PuBa9z9AuBCYJWZXT6mza1Am7svBr4HfCexZY7vmVeamVdZyJKZJVP1liIiaW/CcPeortjT3NjDxzS7CfhZbPsB4FqboqkrHb2DlBbkaqaMiMgogcbczSzbzLYBx4kukL1pTJNa4DUAdx8COoC3TDo3s9VmttnMNjc1NZ1Z5URPpj79SgvXLNX920VERgsU7u4ecfcLgXnApWa2fEyTeN3msb173H2Nuze4e0NNzZkH8qHWHtzhnWfPPOOvJSISJpOaLePu7cB6YNWYXYeB+QBmlgOUA60JqC8QjciIiLxZkNkyNWZWEdsuBK4DXhzTbC3wsdj2zcB/uPtbeu6J1j0wBECOFsMWEXmTnABt5gA/M7Nsor8MfuXuj5rZt4DN7r4WuAe418z2Ee2xfyhpFY+y+UAr+TlZnDunbCreTkQkY0wY7u6+HVgZ5/U7Rm33AR9IbGkTa+sZpKo4j4Lc7Kl+axGRtJaxV6i6O9sPtzOnojDVpYiIpJ2MDffXT/Tx0rEu3nv+nFSXIiKSdjI23Ici0fO1ZYVaVk9EZKyMDffkz8UREclcGRvuuxs7AJirdVNFRN4iY8N9y8E28nKyuHThjFSXIiKSdjI23Icd8rKzyMnO2EMQEUmajE3GYQ26i4iMK2PD/cXGTuZVao67iEg8mRvur5/gogWVqS5DRCQtZWy4O5CrG4aJiMSVkeHu7kSGNeYuIjKejAz3LQfb6OwbYtlc3Q1SRCSejA13gFXn6b4yIiLxZGS4jwzI5OVkZPkiIkkXZCWm+Wa2zsz2mNkuM7stTptyM/utmb0Qa/OJ5JQrIiJBBFmJaQi43d23mlkpsMXMnnD33aPa/D2w293fZ2Y1wF4zu8/dB5JRtIiInNqEPXd3b3T3rbHtTmAPUDu2GVBqZgaUEF1qbyjBtYqISEBBeu4nmVk90SX3No3Z9QOii2QfBUqB/+zuwwmoL67egUisnmS9g4hIZgt8RtLMSoAHgS+4+4kxu98NbAPmAhcCPzCzt8xTNLPVZrbZzDY3NTWddtFPv9LM8toyrZ0qIjKOQOFuZrlEg/0+d38oTpNPAA951D7gVWDp2EbuvsbdG9y9oaam5rSLbuzo4+yZpaf9+SIiYRdktowB9wB73P2ucZodAq6NtZ8FnAPsT1SR8QtL6lcXEcloQcbcrwRuAXaY2bbYa18F6gDc/W7g28BPzWwH0dj9irs3J6FeEREJYMJwd/cNTNBPdvejwPWJKkpERM6MLvEUEQmhjAv3vsEIrd0DlOZPahaniMi0knHh/tyBVnoGIly9dGaqSxERSVsZF+4jFzBVl+SnuBIRkfSVceEuIiITU7iLiISQwl1EJIQU7iIiIaRwFxEJIYW7iEgIKdxFREJI4S4iEkIKdxGREFK4i4iEUMaFu6e6ABGRDJBx4T5Ci2OLiIwvyDJ7881snZntMbNdZnbbOO2uMrNtsTZ/THypIiISVJCbog8Bt7v7VjMrBbaY2RPuvnukgZlVAD8CVrn7ITPT/XhFRFJowp67uze6+9bYdiewB6gd0+xvgIfc/VCs3fFEFyoiIsFNaszdzOqBlcCmMbvOBirNbL2ZbTGzjyamPBEROR2B16ozsxLgQeAL7n4izte5GLgWKASeMbON7v7SmK+xGlgNUFdXdyZ1i4jIKQTquZtZLtFgv8/dH4rT5DDwuLt3u3sz8BRwwdhG7r7G3RvcvaGmpuZM6hYRkVMIMlvGgHuAPe5+1zjNHgHebmY5ZlYEXEZ0bF5ERFIgyLDMlcAtwA4z2xZ77atAHYC73+3ue8zscWA7MAz8xN13JqNgERGZ2ITh7u4bgAkvGXL3O4E7E1GUiIicmYy7QtV1/wERkQllXLiPsIn/mBARmbYyNtxFRGR8CncRkRBSuIuIhJDCXUQkhBTuIiIhpHAXEQkhhbuISAgp3EVEQkjhLiISQgp3EZEQysBw181lREQmkoHhHmW6tYyIyLgyNtxFRGR8CncRkRAKsszefDNbZ2Z7zGyXmd12iraXmFnEzG5ObJkiIjIZQZbZGwJud/etZlYKbDGzJ9x99+hGZpYNfAf4QxLqFBGRSZiw5+7uje6+NbbdSXTh69o4TT8PPAgcT2iFIiIyaZMaczezemAlsGnM67XA+4G7J/j81Wa22cw2NzU1Ta5SEREJLHC4m1kJ0Z75F9z9xJjd/wp8xd0jp/oa7r7G3RvcvaGmpmby1YqISCBBxtwxs1yiwX6fuz8Up0kDcL9FJ59XAzeY2ZC7P5ywSkVEJLAJw92iiX0PsMfd74rXxt0Xjmr/U+BRBbuISOoE6blfCdwC7DCzbbHXvgrUAbj7KcfZE8119wERkQlNGO7uvgEIfLG/u3/8TAoKSrcfEBEZn65QFREJIYW7iEgIKdxFREJI4S4iEkIKdxGREFK4i4iEkMJdRCSEFO4iIiGkcBcRCSGFu4hICGVcuOvWMiIiE8u4cB9hwW93IyIy7WRsuIuIyPgU7iIiIaRwFxEJoQnD3czmm9k6M9tjZrvM7LY4bf7WzLbHHk+b2QXJKVdERIIIshLTEHC7u281s1Jgi5k94e67R7V5FXinu7eZ2XuANcBlSahXREQCCLISUyPQGNvuNLM9QC2we1Sbp0d9ykZgXoLrFBGRSZjUmLuZ1QMrgU2naHYr8NjplyQiImcqyLAMAGZWAjwIfMHdT4zT5mqi4f62cfavBlYD1NXVTbpYEREJJlDP3cxyiQb7fe7+0DhtVgA/AW5y95Z4bdx9jbs3uHtDTU3N6dYsIiITCDJbxoB7gD3uftc4beqAh4Bb3P2lxJb4Zq77D4iITCjIsMyVwC3ADjPbFnvtq0AdgLvfDdwBVAE/iv4uYMjdGxJf7htMdx8QERlXkNkyG+DUN3Jx908Cn0xUUSIicmZ0haqISAgp3EVEQkjhLiISQgp3EZEQUriLiISQwl1EJIQU7iIiIaRwFxEJIYW7iEgIBb4rZLq4btlMtn79XZQVZFzpIiJTJuMSMj8nm/yc7FSXISKS1jQsIyIyhQaGhtn2WlvS30fhLiIyxTbub036eyjcRURCSOEuIhJCCncRkRAKsszefDNbZ2Z7zGyXmd0Wp42Z2ffNbJ+ZbTezi5JTroiIBBFkKuQQcLu7bzWzUmCLmT3h7rtHtXkPsCT2uAz4t9hHERFJgQl77u7e6O5bY9udwB6gdkyzm4Cfe9RGoMLM5iS8WhERCWRSY+5mVg+sBDaN2VULvDbq+WHe+gtARESmSOArVM2sBHgQ+IK7nxi7O86neJyvsRpYDVBXVzeJMkVEwuEb71vGZQurkv4+gXruZpZLNNjvc/eH4jQ5DMwf9XwecHRsI3df4+4N7t5QU1NzOvWKiGS0T1y5kGVzy5L+PkFmyxhwD7DH3e8ap9la4KOxWTOXAx3u3pjAOkVEZBKCDMtcCdwC7DCzbbHXvgrUAbj73cDvgRuAfUAP8InElyoiIkFNGO7uvoH4Y+qj2zjw94kqSkREzoyuUBURCSGFu4hICCncRURCSOEuIhJCCncRkRCy6ESXFLyxWRNw8DQ/vRpoTmA56UjHGA5hP8awHx+k3zEucPcJrwJNWbifCTPb7O4Nqa4jmXSM4RD2Ywz78UHmHqOGZUREQkjhLiISQpka7mtSXcAU0DGGQ9iPMezHBxl6jBk55i4iIqeWqT13ERE5hbQOdzNbZWZ7Ywtv/7c4+/PN7Jex/ZtiK0VllADH+EUz2x1bePzfzWxBKuo8ExMd46h2N5uZm1lGzUwIcnxm9sHY/+MuM/vFVNd4pgJ8n9aZ2Tozez72vXpDKuo8XWb2f83suJntHGe/mdn3Y8e/3cwumuoaJ83d0/IBZAOvAIuAPOAFYNmYNp8F7o5tfwj4ZarrTsIxXg0UxbY/E8ZjjLUrBZ4CNgINqa47wf+HS4DngcrY85mprjsJx7gG+ExsexlwINV1T/IY3wFcBOwcZ/8NwGNE75B7ObAp1TVP9EjnnvulwD533+/uA8D9RBfiHu0m4Gex7QeAa2OLi2SKCY/R3de5e0/s6Uaiq1xlkiD/jwDfBr4L9E1lcQkQ5Pg+BfzQ3dsA3P34FNd4poIcowMjywuVE2cltnTm7k8BradochPwc4/aCFSY2Zypqe70pHO4B1l0+2Qbdx8COoDkL06YOJNdWPxWor2HTDLhMZrZSmC+uz86lYUlSJD/w7OBs83sz2a20cxWTVl1iRHkGP8J+IiZHSa6eM/np6a0KTPZn9WUC7xAdgoEWXQ70MLcaSxw/Wb2EaABeGdSK0q8Ux6jmWUB3wM+PlUFJViQ/8McokMzVxH9y+tPZrbc3duTXFuiBDnGDwM/dff/aWZXAPfGjnE4+eVNiYzLmnTuuQdZdPtkGzPLIfrn4Kn+tEo3gRYWN7PrgK8BN7p7/xTVligTHWMpsBxYb2YHiI5nrs2gk6pBv08fcfdBd38V2Es07DNFkGO8FfgVgLs/AxQQvSdLWAT6WU0n6RzuzwFLzGyhmeURPWG6dkybtcDHYts3A//hsbMfGWLCY4wNWfyYaLBn2lgtTHCM7t7h7tXuXu/u9UTPK9zo7ptTU+6kBfk+fZjoiXHMrJroMM3+Ka3yzAQ5xkPAtQBmdi7RcG+a0iqTay3w0dismcuBDndvTHVRp5TqM7oTnMG+AXiJ6Jn6r8Ve+xbRH36IfgP9mujC3M8Ci1JdcxKO8UngGLAt9lib6poTfYxj2q4ng2bLBPw/NOAuYDewA/hQqmtOwjEuA/5MdCbNNuD6VNc8yeP7f0AjMEi0l34r8Gng06P+D38YO/4dmfA9qitURURCKJ2HZURE5DQp3EVEQkjhLiISQgp3EZEQUriLiISQwl1EJIQU7iIiIaRwFxEJof8PzwNdoNbFYzUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bat_dict['b3c43']['cycles']['10']['Qd'], bat_dict['b3c43']['cycles']['10']['V'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('batch3.pkl','wb') as fp:\n",
    "        pickle.dump(bat_dict,fp)"
   ]
  }
 ],
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